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--- |
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base_model: lnxdx/B4_1000_1e-5_hp-myself-2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: C2_1000_1e-5_hp-myself-2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# C2_1000_1e-5_hp-myself-2 |
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This model is a fine-tuned version of [lnxdx/B4_1000_1e-5_hp-myself-2](https://huggingface.co/lnxdx/B4_1000_1e-5_hp-myself-2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss on ShEMO train set: 0.7065 |
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- Loss on ShEMO dev set: 0.6634 |
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- WER on ShEMO train set: 26.52 |
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- WER on ShEMO dev set: 30.87 |
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- WER on Common Voice 13 test set: 19.43 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 1000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.7564 | 0.62 | 100 | 0.6705 | 0.3131 | |
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| 0.7761 | 1.25 | 200 | 0.6664 | 0.3140 | |
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| 0.7722 | 1.88 | 300 | 0.6573 | 0.3137 | |
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| 0.7035 | 2.5 | 400 | 0.6627 | 0.3157 | |
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| 0.7026 | 3.12 | 500 | 0.6834 | 0.3107 | |
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| 0.7213 | 3.75 | 600 | 0.6561 | 0.3169 | |
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| 0.6996 | 4.38 | 700 | 0.6664 | 0.3096 | |
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| 0.7146 | 5.0 | 800 | 0.6593 | 0.3148 | |
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| 0.7071 | 5.62 | 900 | 0.6646 | 0.3125 | |
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| 0.7065 | 6.25 | 1000 | 0.6634 | 0.3107 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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